{SLmetrics} Version 0.1-1 💪

* Explicit `NA`-handling 🛠️  (#10)

* [UPDATE] `accuracy()`-function

* The `accuracy()`-function now handles missings values according to na.rm
* The `accuracy()`-function uses `std::vector` to offset the loss in speed from missing values checking
* Updated tools; the modifyRcppExports is now more dynamic and uses regex instead of hardcoded functions.

NOTE: All checks passed locally.

* [UPDATE] `zerooneloss()`-function 🚀

* The `zerooneloss()`-function now accepts the argument `na.rm`
* Internal optimaztion of the function to make it faster

* [DOCUMENTATION] Updated NEWS 📚

* The NEWS have been updated to reflect the new changes.

* [UPDATE] `RMSE()`- `RMSLE()`- and `huberloss()` 🚀

* All functions now has an argument `na.rm` to explicitly handle missing values.
* Migrated to std::vector instead of NumericVector
* Fixed a small bug in `accuracy.Rd`

* [UPDATE] Unit-tests and NA-handlings 🔥

* Unit-tests: The unit-tests now tests if misssing values are being handled according to the `na.rm`-argument
* NA-handling in functions: `rsq()`-, `ccc()`-, `mae()`-, `mape()`-, `mse()`-, `pinball()`-, `rae()`-, `rmse()`-, `rrmse()`- and `smape()`-functions now all uses `std::vector` instead of `NumericVector`, and has explicit NA-handling.

All tests passed locally.

* [VERSION-BUMP] 0.1-0 ---> 0.1-1 🪜

> [!NOTE]
>
> Checks fail on PR for OSX. But all else passes.

* [BUG-FIX] `plot()`-method for `ROC()` and `prROC()` 🔨 (#11)

* The plots are now correctly displaying lines when `panels = FALSE`
* NEWS.md updated accordingly.

.gitignore

@@ -49,8 +49,8 @@ po/*~
 rsconnect/
 .Rproj.user
 
-# playground
-playground/
+# sandbox
+sandbox/
 docs
 inst/doc
 

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: SLmetrics
 Title: Machine Learning Performance Evaluation on Steroids
-Version: 0.1-0
+Version: 0.1-1
 Authors@R: c(
     person(
       given   = "Serkan", 

NEWS.Rmd

@@ -15,14 +15,90 @@ knitr::opts_chunk$set(
 set.seed(1903) 
 ```
 
-# Version 0.1-0
+# Version 0.1-1
 
-> Version 0.1-0 is considered pre-release of {SLmetrics}. We do not
+> Version 0.1-1 is considered pre-release of {SLmetrics}. We do not
 > expect any breaking changes, unless a major bug/issue is reported and its nature
 > forces breaking changes.
 
 ## General
 
+* **Backend changes:** All pair-wise metrics arer moved from {Rcpp} to C++, this have reduced execution time by half. All pair-wise metrics are now faster.
+
+## Improvements
+
+* **NA-controls:** All pair-wise metrics that doesn't have a `micro`-argument were handling missing values as according to C++ and {Rcpp} internals. See [Issue](https://github.com/serkor1/SLmetrics/issues/8). Thank you @EmilHvitfeldt for pointing this out. This has now been fixed so functions uses an `na.rm`-argument to explicitly control for this. See below,
+
+```{r}
+# 1) define factors
+actual    <- factor(c("no", "yes"))
+predicted <- factor(c(NA, "no"))
+
+# 2) accuracy with na.rm = TRUE
+SLmetrics::accuracy(
+    actual    = actual,
+    predicted = predicted,
+    na.rm     = TRUE
+)
+
+# 2) accuracy with na.rm = FALSE
+SLmetrics::accuracy(
+    actual    = actual,
+    predicted = predicted,
+    na.rm     = FALSE
+)
+```
+
+## Bug-fixes
+
+* The `plot.prROC()`- and `plot.ROC()`-functions now adds a line to the plot when `panels = FALSE`. See Issue https://github.com/serkor1/SLmetrics/issues/9.
+
+```{r}
+# 1) define actual
+# classes
+actual <- factor(
+  sample(letters[1:2], size = 100, replace = TRUE)
+)
+
+# 2) define response
+# probabilities
+response <- runif(100)
+
+# 3) calculate
+# ROC and prROC
+
+# 3.1) ROC
+roc <- SLmetrics::ROC(
+    actual,
+    response
+)
+
+# 3.2) prROC
+prroc <- SLmetrics::prROC(
+    actual,
+    response
+)
+
+# 4) plot with panels
+# FALSE
+par(mfrow = c(1,2))
+plot(
+  roc,
+  panels = FALSE
+)
+
+plot(
+    prroc,
+    panels = FALSE
+)
+```
+
+
+
+# Version 0.1-0
+
+## General
+
 * {SLmetrics} is a collection of Machine Learning performance evaluation functions for supervised learning. Visit the online documentation on [GitHub Pages](https://serkor1.github.io/SLmetrics/).
 
 ## Examples

NEWS.md

@@ -1,10 +1,110 @@
 
-# Version 0.1-0
+# Version 0.1-1
 
-> Version 0.1-0 is considered pre-release of {SLmetrics}. We do not
+> Version 0.1-1 is considered pre-release of {SLmetrics}. We do not
 > expect any breaking changes, unless a major bug/issue is reported and
 > its nature forces breaking changes.
 
+## General
+
+  - **Backend changes:** All pair-wise metrics arer moved from {Rcpp} to
+    C++, this have reduced execution time by half. All pair-wise metrics
+    are now faster.
+
+## Improvements
+
+  - **NA-controls:** All pair-wise metrics that doesn’t have a
+    `micro`-argument were handling missing values as according to C++
+    and {Rcpp} internals. See
+    [Issue](https://github.com/serkor1/SLmetrics/issues/8). Thank you
+    @EmilHvitfeldt for pointing this out. This has now been fixed so
+    functions uses an `na.rm`-argument to explicitly control for this.
+    See below,
+
+<!-- end list -->
+
+``` r
+# 1) define factors
+actual    <- factor(c("no", "yes"))
+predicted <- factor(c(NA, "no"))
+
+# 2) accuracy with na.rm = TRUE
+SLmetrics::accuracy(
+    actual    = actual,
+    predicted = predicted,
+    na.rm     = TRUE
+)
+```
+
+    #> [1] 0
+
+``` r
+# 2) accuracy with na.rm = FALSE
+SLmetrics::accuracy(
+    actual    = actual,
+    predicted = predicted,
+    na.rm     = FALSE
+)
+```
+
+    #> [1] NaN
+
+## Bug-fixes
+
+  - The `plot.prROC()`- and `plot.ROC()`-functions now adds a line to
+    the plot when `panels = FALSE`. See Issue
+    <https://github.com/serkor1/SLmetrics/issues/9>.
+
+<!-- end list -->
+
+``` r
+# 1) define actual
+# classes
+actual <- factor(
+  sample(letters[1:2], size = 100, replace = TRUE)
+)
+
+# 2) define response
+# probabilities
+response <- runif(100)
+
+# 3) calculate
+# ROC and prROC
+
+# 3.1) ROC
+roc <- SLmetrics::ROC(
+    actual,
+    response
+)
+
+# 3.2) prROC
+prroc <- SLmetrics::prROC(
+    actual,
+    response
+)
+
+# 4) plot with panels
+# FALSE
+par(mfrow = c(1,2))
+plot(
+  roc,
+  panels = FALSE
+)
+```
+
+![](NEWS_files/figure-gfm/unnamed-chunk-2-1.png)<!-- -->
+
+``` r
+plot(
+    prroc,
+    panels = FALSE
+)
+```
+
+![](NEWS_files/figure-gfm/unnamed-chunk-2-2.png)<!-- -->
+
+# Version 0.1-0
+
 ## General
 
   - {SLmetrics} is a collection of Machine Learning performance
@@ -25,7 +125,7 @@ print(
 )
 ```
 
-    #>  [1] b a b b a c b c c a
+    #>  [1] a a a b c a b c c a
     #> Levels: a b c
 
 ``` r
@@ -37,7 +137,7 @@ print(
 )
 ```
 
-    #>  [1] c c a b a b c c a c
+    #>  [1] c c b b a a b b c b
     #> Levels: a b c
 
 ``` r
@@ -55,16 +155,16 @@ summary(
     #> Confusion Matrix (3 x 3) 
     #> ================================================================================
     #>   a b c
-    #> a 1 0 2
-    #> b 1 1 2
+    #> a 1 2 2
+    #> b 0 2 0
     #> c 1 1 1
     #> ================================================================================
     #> Overall Statistics (micro average)
-    #>  - Accuracy:          0.30
-    #>  - Balanced Accuracy: 0.31
-    #>  - Sensitivity:       0.30
-    #>  - Specificity:       0.65
-    #>  - Precision:         0.30
+    #>  - Accuracy:          0.40
+    #>  - Balanced Accuracy: 0.51
+    #>  - Sensitivity:       0.40
+    #>  - Specificity:       0.70
+    #>  - Precision:         0.40
 
 ``` r
 # 2) calculate false positive
@@ -75,7 +175,7 @@ SLmetrics::fpr(
 ```
 
     #>         a         b         c 
-    #> 0.2857143 0.1666667 0.5714286
+    #> 0.2000000 0.3750000 0.2857143
 
 ### Supervised regression metrics
 
@@ -96,4 +196,4 @@ SLmetrics::huberloss(
 )
 ```
 
-    #> [1] 0.394088
+    #> [1] 0.4757045